Latest / Automated with Brian Heater / Dr. Ayanna Howard on Human-Centered Robotics, AI Guardrails, and Physical AI

Transcript

Dr. Ayanna Howard on Human-Centered Robotics, AI Guardrails, and Physical AI · Automated with Brian Heater

• 0:00
  SPEAKER_00: My fear is actually around these LLMs and agentic AI.
• 0:06
  SPEAKER_00: You're not putting in the guardrails necessarily.
• 0:09
  SPEAKER_00: I talk about things like military AI.
• 0:11
  SPEAKER_00: AI, at some point, will be able to actually hold the trigger.
• 0:16
  SPEAKER_01: The minute you tell somebody that you know you're a roboticist or your internet around AI, people's ideas of robotics are both way in the future and way in the past at the same time.
• 0:27
  SPEAKER_00: The technologies that are coming out today that seem to be highly advanced.
• 0:32
  SPEAKER_00: We were working on some of these things 25 plus years ago.
• 0:35
  SPEAKER_00: We now think, oh, it can do everything.
• 0:37
  SPEAKER_00: And the fact is that we are still trying to figure out how to do this in the real world so that we have general purpose robotics.
• 0:45
  SPEAKER_01: It sounds like you understand what the really big problems are.
• 0:48
  SPEAKER_00: When I look at the current humanoid robots like Figure AI this live stream, that is not the real world.
• 0:55
  SPEAKER_00: If you ever go into a logistics center, I mean it's a massive mess.
• 0:59
  SPEAKER_00: Manipulation is still hard.
• 1:01
  SPEAKER_01: You're seeing these incredibly cool demos, but these things aren't coming to the store and coming to your house tomorrow.
• 1:07
  SPEAKER_01: So I would say Hello everyone, and welcome to another episode of Automated with Brian Heater, who is me, Brian Heater.
• 1:27
  SPEAKER_01: I am the managing editor at the Association for Advancing Automation.
• 1:32
  SPEAKER_01: And I am excited to bring you a conversation with someone that we've been working on booking for the show for a long time now.
• 1:40
  SPEAKER_01: Dr.
• 1:41
  SPEAKER_01: Iana Howard is the Dean of the Ohio State University School of Engineering, among many other things.
• 1:48
  SPEAKER_01: We cover a lot of ground during this conversation, but uh one thing we keep coming back to that's been, I would say, a constant through her academic and professional career is the push to apply robotics and automation for the common good.
• 2:05
  SPEAKER_01: It's a really great chat, and I hope that you come out of it wanting to make the world a slightly better place.
• 2:11
  SPEAKER_01: I know that I did.
• 2:12
  SPEAKER_01: Thank you so much for listening.
• 2:14
  SPEAKER_01: If you've been enjoying the show, it would be a huge help if you would like and subscribe and tell a couple of friends.
• 2:21
  SPEAKER_01: And while you're at it, why not subscribe to the newsletter over at automated.fm.
• 2:26
  SPEAKER_01: Now please enjoy this conversation with Dr.
• 2:28
  SPEAKER_01: Iana Howard.
• 2:31
  SPEAKER_01: Vision and AI are changing what's possible in industrial automation from quality inspection and predictive maintenance to robotics, logistics, medical devices, aerospace, and manufacturing.
• 2:42
  SPEAKER_01: At the Advanced Vision and AI conference, you'll hear from industry leaders, see real-world AI applications in action, explore the latest in machine vision, computer vision, sensors, edge computing, and automation technology.
• 2:55
  SPEAKER_01: You'll also be able to connect with the people who are shaping what comes next.
• 2:59
  SPEAKER_01: Whether you're building, buying, or implementing these systems, this is a chance to stay ahead of the curve and see how vision and AI are moving from promise to practical impact.
• 3:09
  SPEAKER_01: Learn more and make sure to buy your ticket early at the Advanced Vision and AI conference September 23rd and 24th in Santa Clara.
• 3:20
  SPEAKER_01: As a general rule, I try not to open an interview with the the heaviest subject possible.
• 3:27
  SPEAKER_01: But as I was going through your very, very CV, one of the things jumped out at me, which was the snowmoat robots that you were working on with NASA.
• 3:37
  SPEAKER_01: And I had this moment where I think the human brain only has the capacity for so many horrible things happening at any given moment that I was like, oh yeah.
• 3:49
  SPEAKER_01: Also the um this the the ice caps of paramilitary.
• 3:53
  SPEAKER_01: No.
• 3:54
  SPEAKER_01: Like that's also still a thing that's happening right now that we still have to think about on top of everything.
• 3:59
  SPEAKER_01: Is that is that work that you're still active with, that you're still engaging with?
• 4:03
  SPEAKER_00: I'm not.
• 4:04
  SPEAKER_00: So um that was my life when I was working really on science-driven robotics.
• 4:09
  SPEAKER_00: Um and so what that meant was taking rovers, taking robots in the field to help scientists collect data, everything from underwater robotics to glacial robotics, to of course prior when I was at NASA thinking about future Mars rover exploration.
• 4:26
  SPEAKER_00: So I transitioned from science-driven robotics maybe in I would say 2012-ish timeframe, uh, where I finally said, okay, I'm gonna put up my my hat on that and go 100% into healthcare robotics.
• 4:42
  SPEAKER_01: It's tough, right?
• 4:43
  SPEAKER_01: I mean, because it's not, it's you can never, in something like that, you can never be like, all right, problem solved.
• 4:48
  SPEAKER_00: You can't.
• 4:49
  SPEAKER_00: Um, and even now, I still, when Artemis, you know, I'm like, I was glued, like, okay, what's going on?
• 4:54
  SPEAKER_00: Oh, we're gonna we're gonna actually go back to the moon.
• 4:57
  SPEAKER_00: Okay, that's gonna be rovers.
• 4:58
  SPEAKER_00: Like, you have to have rovers with astronauts when we do the colonization of the moon.
• 5:02
  SPEAKER_00: So I'm still always interested and always fascinated and start thinking about these things.
• 5:07
  SPEAKER_00: Uh, I just don't have the research team that works on it anymore.
• 5:11
  SPEAKER_01: Yeah, maybe healthcare at least is something uh hopefully obviously broken in its own special way, but maybe that's something a little more immediate that you can effect change on.
• 5:23
  SPEAKER_00: Well, even with snow moats, the reason that I was so fascinated with snow moats was it was helping Earth, right?
• 5:30
  SPEAKER_00: Even though it was NASA funded, it was really thinking about how do we ensure that we survive the, you know, the climate apocalypse uh here on Earth.
• 5:39
  SPEAKER_00: And so I would tell you, working on snow moats was my start into thinking about Earth as a planet, and of course, who's on Earth?
• 5:48
  SPEAKER_00: Earthlings, humans.
• 5:50
  SPEAKER_00: So that was actually part of the transition of thinking about healthcare.
• 5:53
  SPEAKER_00: And it's not really healthcare per se, because of the healthcare aspect, is around how can we use technology and robotics to engage and help with the human condition, whether it's with climate change or whether with our own personal quality of life.
• 6:10
  SPEAKER_01: That's a really interesting point.
• 6:11
  SPEAKER_01: What do they they call it the overview effect, I think, when astronauts go up into space and see Earth for the first time and suddenly they like recontextualize it.
• 6:19
  SPEAKER_01: So you felt like you had this moment where you realized, I guess, what a uh sort of a uh, I guess, fragile ecosystem we're living in.
• 6:30
  SPEAKER_00: Correct.
• 6:31
  SPEAKER_00: Um, and when you think about the reasons why we are here, it is because of people.
• 6:37
  SPEAKER_00: It's because of humans.
• 6:39
  SPEAKER_00: Um, and so as a technologist, it's what can I do to help us humans continue?
• 6:45
  SPEAKER_00: And I think it's and when I think about technologists in general, I think it is our responsibility to think about how we use our powers to help humanity, help people.
• 6:54
  SPEAKER_01: So that's definitely been an overarching theme in much of our work, but let's back up a little bit.
• 6:59
  SPEAKER_01: When you really got into this, like early, early little kid, uh my understanding is bionic woman.
• 7:07
  SPEAKER_00: Yes, it was a bionic woman.
• 7:10
  SPEAKER_00: Um, and so I would say it was, you know, one of those hindsights, but back in the day, uh, middle school, in fact, it's when we all had to write those essays around what we wanted to do for our career, and you had to figure out she said I want to be a bionic woman?
• 7:24
  SPEAKER_00: I well, I said I wanted to build the bionic woman.
• 7:27
  SPEAKER_00: Okay.
• 7:27
  SPEAKER_00: Uh not be, build the bionic woman.
• 7:29
  SPEAKER_00: And I remember thinking that, you know, it was fascinating.
• 7:32
  SPEAKER_00: I was, I was into science fiction and science fantasy.
• 7:35
  SPEAKER_00: So anything that had robots or superheroes or space.
• 7:38
  SPEAKER_00: And the bionic woman really uh just talked to me.
• 7:42
  SPEAKER_00: It was like, oh, you know, middle school girl thinking about what I can do.
• 7:45
  SPEAKER_00: And I was like, I I wanna I want to do that.
• 7:48
  SPEAKER_00: And I remember my teacher saying, Well, you know, building bionic woman is not like a career, like you have to be something.
• 7:55
  SPEAKER_00: And so I decided that I wanted to be a doctor because in the bionic woman, there were the doctors that actually put the parts on and did all of these things.
• 8:06
  SPEAKER_00: And I then went into high school, I took biology.
• 8:10
  SPEAKER_00: And I thought, yeah, being being a medical doctor, that was not in my career horizon.
• 8:17
  SPEAKER_00: And that's when it converted to, okay, I'm gonna be a um at the time cybernetics engineer, which it didn't really exist.
• 8:24
  SPEAKER_00: It was again science fiction, science fantasy kind of term, but that ended up being robotics.
• 8:29
  SPEAKER_01: What was it about the physiological part that didn't really stick with you?
• 8:34
  SPEAKER_00: Um, so with the biology, it was that in that in that day of time when we did the experiments with frogs, we actually had to learn how to kill them in a in a humane way.
• 8:48
  SPEAKER_00: And I just, it just something about killing a living creature, even though it was for science, uh, it just bothered me.
• 8:56
  SPEAKER_00: And I realized that if I had to do that as my entire career to get to that, that that's just was it, it wasn't my passion.
• 9:04
  SPEAKER_00: But the bionics was my passion, thinking about even the attachments.
• 9:08
  SPEAKER_00: I work with the doctors and I would figure out how to connect the actual robotic part to to the person.
• 9:14
  SPEAKER_00: So I had to understand the human, but I didn't have to do the operation.
• 9:18
  SPEAKER_00: And so that's really the reason why the engineering, and it wasn't even just engineering.
• 9:23
  SPEAKER_00: Uh, I chose an undergrad that allowed me to do basically a liberal arts approach to engineering because I didn't even know what robotics really was.
• 9:32
  SPEAKER_00: It wasn't a field, so I had to figure out how to make it up.
• 9:36
  SPEAKER_01: Yeah, you you again, like you effectively invented your undergrad.
• 9:41
  SPEAKER_01: Is that fair?
• 9:43
  SPEAKER_00: Yeah, so uh undergrad Brown University, which has you basically this concept of an open curriculum, even today, which basically means that there are core things you have to have because there's accreditation, but you draft your own major in essence, you put in courses that you're interested in, you think about it.
• 10:02
  SPEAKER_00: And at the time, engineers, if you declared yourself an engineering major, we all took the same courses our first two years.
• 10:09
  SPEAKER_00: So you took dynamics and you took thermo and you took mechanics and you took physics.
• 10:15
  SPEAKER_00: So you can figure out one which you really didn't like and then what you liked.
• 10:19
  SPEAKER_01: But you had the same foundation as everybody else, effectively.
• 10:22
  SPEAKER_00: We we all had the same foundation, and then you specialized your last two years.
• 10:26
  SPEAKER_00: Um, and during that time I realized that the computers and engineering and the programming and the building really would allow me to do the robotics.
• 10:35
  SPEAKER_00: And I started working at NASA at the same time, and so that really highlighted, oh, I'm interested in thinking about now.
• 10:42
  SPEAKER_00: We call it autonomy.
• 10:43
  SPEAKER_00: At the time, it was computer vision, it was programming.
• 10:47
  SPEAKER_00: Um, we really didn't use the word artificial intelligence per se, but there was a book on artificial intelligence, which I also learned from.
• 10:54
  SPEAKER_00: Uh, but basically figured out the courses I needed to become a roboticist.
• 10:58
  SPEAKER_00: Was it machine learning yet?
• 11:01
  SPEAKER_00: Um it was early machine learning.
• 11:03
  SPEAKER_00: So it was neural networks, we learned about neural networks, we learned about um elements of uh what you would call feedback control that that helped in that.
• 11:13
  SPEAKER_00: We learned about uh fuzzy logic, which is all elements of feeding into machine learning.
• 11:20
  SPEAKER_00: So machine learning, as you know, is looking at data, looking at everything from neural networks to reinforcement learning to other techniques.
• 11:27
  SPEAKER_00: Large language models did not exist uh that fit into this machine learning.
• 11:31
  SPEAKER_01: So you started working at NASA during your undergrad?
• 11:35
  SPEAKER_01: I did after my freshman year.
• 11:37
  SPEAKER_01: How'd you get your foot in the door at NASA?
• 11:40
  SPEAKER_00: So uh opportunities and mentorship.
• 11:43
  SPEAKER_00: So my first job job uh when I turned 16, I was worked at Caltech.
• 11:48
  SPEAKER_00: And I was basically, we would call them the database administrator.
• 11:51
  SPEAKER_00: Caltech in their accounting was just bringing in computers, and they needed someone who understands computers.
• 11:58
  SPEAKER_00: I've been working in high school uh tutoring some of the students on how to do programming.
• 12:04
  SPEAKER_00: This was again early 80s.
• 12:08
  SPEAKER_00: Um, and so I was I had a teacher who knew someone at Caltech, and they were trying to find some whiz kid who understood computers.
• 12:17
  SPEAKER_00: And my teacher said, I have someone for you, and she actually can talk too.
• 12:21
  SPEAKER_00: And so I started at Caltech, and Caltech manages JPL, NASA's JPL.
• 12:27
  SPEAKER_00: And so I'd worked there two years, basically after my junior year of high school, senior year of high school.
• 12:34
  SPEAKER_00: I was going to go back, and my supervisor said, No, I need you to be at NASA because that's what you've always wanted to do.
• 12:41
  SPEAKER_00: You wanted to work in robotics.
• 12:43
  SPEAKER_00: Uh, I'm gonna give you the name of someone.
• 12:45
  SPEAKER_00: It's up to you to land it, but I'm gonna give you someone who might be interested in having you come work for them.
• 12:52
  SPEAKER_00: So I interviewed.
• 12:54
  SPEAKER_00: Uh back in the day, you actually had to show up in person, no virtual.
• 12:59
  SPEAKER_00: Uh, interviewed, and he's like, you have the job for next summer.
• 13:02
  SPEAKER_01: I mean, how cool is it at that point in your life?
• 13:05
  SPEAKER_01: I mean, I'm guessing you were what, like 18, 19, maybe for them to say 18.
• 13:10
  SPEAKER_01: 18, you're needed at NASA.
• 13:13
  SPEAKER_01: That must have been a moment, right?
• 13:15
  SPEAKER_00: It was.
• 13:16
  SPEAKER_00: Although, you know, as a if you I work with 17 and 18 year olds all the time.
• 13:21
  SPEAKER_01: Yeah.
• 13:22
  SPEAKER_00: We don't typically think that way.
• 13:25
  SPEAKER_00: Uh, I didn't typically think that way.
• 13:26
  SPEAKER_01: They're just like, yeah, of course I of course NASA needs me.
• 13:30
  SPEAKER_00: Exactly.
• 13:31
  SPEAKER_00: Like most 18-year-olds think that same way as well now.
• 13:34
  SPEAKER_01: So what is recursive learning for deformable object manipulation?
• 13:41
  SPEAKER_00: That was the title of my PhD thesis.
• 13:44
  SPEAKER_00: Back in the day, we had uh really big issues with uh HIV.
• 13:50
  SPEAKER_00: It was on the rise.
• 13:51
  SPEAKER_00: And in hospitals, the way they did waste management is that they would have uh waste linen, for example, would be in one bag, and you'd have just waste, you know, papers and all in another, and you would have the needles in another bag, right?
• 14:05
  SPEAKER_00: We didn't have those very safe containers that are in the hospital rooms.
• 14:09
  SPEAKER_00: And people would have to basically sort them out.
• 14:12
  SPEAKER_00: And so the idea was well, couldn't we get robots to do this automated management of sorting out the bags so that things that were hazardous to people, you put it in one bin, and things that had to go to wash, you put it in another bin.
• 14:26
  SPEAKER_00: So that was the the holy grail of what I was trying to do with my thesis.
• 14:32
  SPEAKER_00: Um, what it came down to was figuring out how people manipulate objects, everything from squishy objects, like bags filled with linen, to hard objects.
• 14:43
  SPEAKER_00: Um, and how do you do this with computer vision?
• 14:45
  SPEAKER_00: So recursive learning was thinking about the way people figure things out and having the robot learn on its own how to figure out how to grab objects in in the real world.
• 14:57
  SPEAKER_01: So this sounds like a case of like a really big real world problem and then realizing like how much needs to get solved in order to get there.
• 15:06
  SPEAKER_00: Exactly.
• 15:08
  SPEAKER_00: It was, I I say it was at the end of the day, it ended up being a toy problem.
• 15:13
  SPEAKER_00: I had two manipulators with sensors.
• 15:15
  SPEAKER_00: I had to build the whole thing because of course you couldn't go out and buy manipulators or buy robot arms.
• 15:20
  SPEAKER_00: Um I remember I had a computer camera, I created 3D uh computer vision, but the way it worked is I had multiple cameras.
• 15:28
  SPEAKER_00: I created uh we could do a video frame grabber, which they use in entertainment, and I figured out how to daisy chain it, so I had 3D computer vision.
• 15:36
  SPEAKER_00: So it was really an engineering, like thinking about in my garage trying to build this prototype so I can then evaluate um things like using neural networks.
• 15:46
  SPEAKER_00: In fact, my thesis use neural networks to to learn how to do this.
• 15:50
  SPEAKER_00: Um, of course, it was also had to be very fast using a neural network, which was crazy.
• 15:57
  SPEAKER_00: And computer vision, where you had these pixels of you know eight by eight and had to figure out what was an object in this little tiny image.
• 16:06
  SPEAKER_00: Um now a high school student could probably figure it out.
• 16:11
  SPEAKER_00: But then it was it was nothing existed.
• 16:13
  SPEAKER_01: Yeah, a high school student could probably figure it out, but at the same time, it's like we we still don't exactly have technology solving that problem.
• 16:23
  SPEAKER_01: We don't.
• 16:24
  SPEAKER_01: In the real world, really.
• 16:25
  SPEAKER_01: I mean, right?
• 16:26
  SPEAKER_01: I mean, this is this is an interesting thing about tech, and this is something that I'm sure like the minute you tell somebody that you know you're a roboticist or you're in and out around AI is you find that people both people's ideas of robotics are both way in the future and way in the past at the same time.
• 16:43
  SPEAKER_00: They are.
• 16:44
  SPEAKER_00: Um, and I would say the technologies that are coming out today that seem to be highly advanced.
• 16:52
  SPEAKER_00: Uh there were elements of it 25, I mean been out 25 plus years.
• 16:57
  SPEAKER_00: Like we were working on some of these things 25 plus years ago.
• 17:01
  SPEAKER_00: The computes were different, the sensors were different, the vision was different, how fast things moved were different.
• 17:08
  SPEAKER_00: But the basic research, the fundamentals, were very, very much the same.
• 17:13
  SPEAKER_00: Now we we think, oh, everything is solved.
• 17:16
  SPEAKER_00: Like I can talk to my computer now and it knows everything about me, but it actually doesn't know 100%.
• 17:22
  SPEAKER_00: It's also recursive learning.
• 17:24
  SPEAKER_00: It's recursively learning a little bit more every day about you.
• 17:28
  SPEAKER_00: And then you add in the physical with the physical embodiment, which they now use physical AI, which is basically robotics with the brain.
• 17:36
  SPEAKER_01: Yeah.
• 17:36
  SPEAKER_00: We now think, oh, it can do everything just like my chatbot can.
• 17:40
  SPEAKER_00: And the fact is, is that we are still trying to figure out how to do this in the real world so that we have general purpose robotics.
• 17:48
  SPEAKER_00: We're not there, but the trajectory is very, very positive.
• 17:52
  SPEAKER_00: I'm actually very hopeful.
• 17:54
  SPEAKER_01: Yeah, I mean, especially working in, you know, at that time, specifically neural networks, and it sounds like to a certain extent grasping, like you understand what the really big problems are.
• 18:07
  SPEAKER_00: I do.
• 18:08
  SPEAKER_00: Um, and when I look at the current, you know, humanoid robots that are um going about like Figure AI had this live stream where you had this robot kind of grasping but also pushing things around.
• 18:22
  SPEAKER_01: Um, mostly on the mostly on the conveyor belt.
• 18:26
  SPEAKER_00: Flat, right?
• 18:27
  SPEAKER_00: Like as a flat conveyor, if you look at the number of objects that are being manipulated, um, it's not universal.
• 18:34
  SPEAKER_00: It's not like in a home.
• 18:35
  SPEAKER_00: It's like a couple of packages.
• 18:37
  SPEAKER_00: They all seem kind of like the same size, a couple of boxes, they're all relatively in the same form factor.
• 18:42
  SPEAKER_01: Yeah, knock them over, get the butt code on the bottom, right?
• 18:46
  SPEAKER_00: Exactly.
• 18:47
  SPEAKER_00: Um, but I will tell you, in some senses, that is amazing because it was at least consistent enough, but yet that is not the real world.
• 18:58
  SPEAKER_00: If you ever go into a logistics center, I mean it's a massive mess, right?
• 19:02
  SPEAKER_00: Like it has everything.
• 19:04
  SPEAKER_00: I mean, imagine when you're mailing things.
• 19:05
  SPEAKER_00: What do you do?
• 19:06
  SPEAKER_00: I'm like, oh, do I have a bag somewhere?
• 19:08
  SPEAKER_00: I'm gonna use that bag, just tape something around it, put a label on it, ship it.
• 19:12
  SPEAKER_00: Well, you have that, and then you have the boxes.
• 19:15
  SPEAKER_00: We people, we figure it out.
• 19:17
  SPEAKER_00: Manipulation is still hard.
• 19:19
  SPEAKER_01: Yeah, I the this has been an interesting part of of my of my work.
• 19:25
  SPEAKER_01: You know, the more the the deeper I go and the more I learn about it, of how much of this is hey, like look at how far this has gone, look at how amazing technology is and how much of it needs to be like pouring cold water on it, setting realistic expectations of like, yeah, these you're seeing these incredibly cool demos, but these things aren't coming to the store and coming to your house tomorrow.
• 19:49
  SPEAKER_00: They aren't.
• 19:50
  SPEAKER_00: Um, and part of it is the philosophy of how do you invent in a realm where you still have to have product market fit.
• 20:01
  SPEAKER_00: Um so I actually truly believe if that if you had unlimited resources to do this, we would have a lot more advancements.
• 20:10
  SPEAKER_00: But the fact is, is you have to figure out, you know, how do you make money?
• 20:14
  SPEAKER_00: I can get investment early on, but at some point, if I don't have customers that will pay for it and I can't reduce the cost of my hardware or software platform, then I I become defunct.
• 20:26
  SPEAKER_00: I die.
• 20:26
  SPEAKER_00: That is the nature of business.
• 20:28
  SPEAKER_00: And so when I think about the opportunities, why healthcare, as an example, I'm really in favor of like one, it's still very expensive, irrespective if you use a robotics or not.
• 20:41
  SPEAKER_00: Two, everything the market product fit, I mean, the entire market is the human race.
• 20:46
  SPEAKER_00: Like everyone wants to be healthy.
• 20:48
  SPEAKER_00: So if you can just figure out the price point and the application, you would have a product market fit.
• 20:55
  SPEAKER_00: And of course, you can't kill people because you know, it's healthcare.
• 20:59
  SPEAKER_01: Yeah, you so so so your thesis is a good uh example of how to do robotics right from the standpoint of recognizing a real world problem and then trying to solve it.
• 21:10
  SPEAKER_01: Obviously, you know, the thing that was missing is all the like practical pieces in the middle.
• 21:14
  SPEAKER_01: And too often, you know, we see roboticists going the opposite way, right?
• 21:19
  SPEAKER_01: Cool technology, what you know, what can we fix?
• 21:22
  SPEAKER_01: And one of the the last time you and I spoke, I don't know if you remember this, but I was at TechRunch at the time, so we talked a lot about your time.
• 21:29
  SPEAKER_01: Oh, now it's coming back to you, right?
• 21:34
  SPEAKER_01: Um uh so you know, a lot of the conversation was was around, you know, the startups versus uh versus university, right?
• 21:42
  SPEAKER_01: And and I'm I I'm you have uh spent a lot of time on both sides, especially various universities.
• 21:48
  SPEAKER_01: And I'm kind of curious when it comes to pure innovation and and purely pushing technology forward, is there a pr a preferred venue?
• 22:00
  SPEAKER_00: Um, we need both.
• 22:02
  SPEAKER_00: Um, and and why is this?
• 22:04
  SPEAKER_00: It's because if you think about the innovations, they have to derive from fundamental principles.
• 22:11
  SPEAKER_00: I was talking about, you know, machine learning and computer vision.
• 22:15
  SPEAKER_00: If I wasn't doing that, the things that I was doing back in the day in the lab with the PhD thesis, and I still get referenced with my PhD thesis, and that was in 1999.
• 22:26
  SPEAKER_00: I'm like, I'm being referenced because now they're like, oh, and this is how this does it, and we're gonna do an advancement compared to that, right?
• 22:32
  SPEAKER_00: If that didn't exist, we can have a lot of these other technologies.
• 22:37
  SPEAKER_00: That has to be done in terms of a university because no one else is gonna pay for it, right?
• 22:42
  SPEAKER_00: There's not an investor that's like, yeah, if you do something and 20 years from now I get a return on my investment, I'm good with that.
• 22:49
  SPEAKER_00: Just doesn't happen.
• 22:50
  SPEAKER_00: But for the startups who are hungry, they're the ones that what they say is uh, I have a need, i.e., a market, I have a technology that doesn't necessarily work for that market.
• 23:02
  SPEAKER_00: As a startup, I'm trying to blend the two together.
• 23:05
  SPEAKER_00: Hungry, moving fast, trying to figure it out, and being okay to say, okay, we spend six months on it, that's not gonna work.
• 23:12
  SPEAKER_00: We gotta revolt because uh if we don't have a revolution here, we're gonna die.
• 23:18
  SPEAKER_00: Let's figure it out.
• 23:19
  SPEAKER_00: Sometimes it's even looking at a different market.
• 23:21
  SPEAKER_00: You need both.
• 23:22
  SPEAKER_01: You need both, but one of the interesting things, and and again, this is something that I was especially interested in for obvious reasons back in my my TechCurrence days, is the uh the attempts to bridge the gap, right?
• 23:34
  SPEAKER_01: And and um use So you know, so I I don't it's not necessarily a bad thing, right?
• 23:40
  SPEAKER_01: Because once once you like graduate universities, you gotta get into the real world and and and you do and there are people trying to commercialize these technologies.
• 23:48
  SPEAKER_01: There have been interesting attempts to incubate and accelerate technologies within and around the university system, but you did just let out a really large sigh when I said that.
• 24:02
  SPEAKER_00: The incentives are different.
• 24:04
  SPEAKER_00: So in academia, the incentives are not necessarily to create products that m make have no value immediately.
• 24:14
  SPEAKER_00: Um the academics are you do solid research, it's around publications, it's around receiving grants.
• 24:22
  SPEAKER_00: It is not about making something that can help 10 people and then 20 people and then 30 people.
• 24:29
  SPEAKER_00: Um, so that's the incentives are different.
• 24:33
  SPEAKER_00: The startup incentive is um twofold.
• 24:36
  SPEAKER_00: One is I want to be the unicorn, right?
• 24:39
  SPEAKER_00: Like I want to be the next Meta or the next Microsoft or the next dot dot dot or next figure AI or agility.
• 24:47
  SPEAKER_00: Like that's my goal.
• 24:48
  SPEAKER_00: And so I'm going to work 24-7, I'm gonna live in the garage, um, I'm gonna take, you know,$2 an hour so that at least I can eat some food.
• 24:57
  SPEAKER_00: Like, that's the incentive, is it's the it's the hope of a future.
• 25:04
  SPEAKER_00: I don't see a faculty member saying, I'm gonna work 24-7 in in my garage with the hope that I'm gonna make tenure.
• 25:12
  SPEAKER_00: Like, that's just not gonna happen.
• 25:14
  SPEAKER_00: And so when I think about the startups at the university, the best option and the ones that I see as the most viable are when faculty can support student innovation because the students are the ones that are hungry.
• 25:29
  SPEAKER_00: They don't yet have a paycheck, so they don't realize that you know what it feels like when you make$70,000.
• 25:35
  SPEAKER_00: Like it's a lot different than when you're making$10,000.
• 25:38
  SPEAKER_00: Uh, but you still need that guidance of, well, are you asking the right question, faculty member?
• 25:43
  SPEAKER_00: You know, are you really figuring this out?
• 25:45
  SPEAKER_00: Have you thought about XYZ?
• 25:47
  SPEAKER_00: Are there's something else going on in another field?
• 25:49
  SPEAKER_00: Maybe you should take a look at that and see how they do it and incorporate it in your innovation.
• 25:53
  SPEAKER_00: I think that's the the nice blend, but you still have to figure out how do you incentivize the faculty to want to do that.
• 26:01
  SPEAKER_01: This is actually something that I've never I've never asked anybody, because I I guess for some reason it it never occurred to me to ask, but um, you know, you you know, you've been the dean, you've you've you've run these schools, you've you've been a professor.
• 26:16
  SPEAKER_01: These days, do you find, like given all of the media around VC culture, around startup culture, that a lot of students are coming in to school wanting to be the next meta at the next Microsoft, and and that that can present its own difficulty?
• 26:32
  SPEAKER_00: So I would say not as many as I would have thought.
• 26:36
  SPEAKER_00: If you're in certain cities, like if you're in the Bay Area, I think there's more of that, or if you're in Boston, I think there's more of that.
• 26:42
  SPEAKER_00: But if you look at the summary of all of the universities, at least in the United States, that is not necessarily the case.
• 26:49
  SPEAKER_00: Um, because you're not drinking the water where you're like, okay, startup is part of your culture.
• 26:54
  SPEAKER_00: Most of our students come in thinking, I'm coming to college to get a degree so that I can have a quality of life, i.e., through a job.
• 27:03
  SPEAKER_00: Not necessarily wanting to be a startup.
• 27:06
  SPEAKER_00: I think as a very small percentage that want to be an entrepreneur.
• 27:11
  SPEAKER_00: Believe it or not, but the ones that do want to be an entrepreneur, they're they're doing it not because they want a what we call a lifestyle business.
• 27:19
  SPEAKER_00: They're doing it because they want to be the next unicorn.
• 27:21
  SPEAKER_01: Yeah, the the ones who do want to, I mean, I've been noticing this in hindsight very obvious trend, but the ones who do want to be an entrepreneur are the ones that are uh double majoring.
• 27:31
  SPEAKER_00: They are.
• 27:32
  SPEAKER_00: They are.
• 27:33
  SPEAKER_00: Um, I think it's because of two things.
• 27:36
  SPEAKER_00: One is they realize you can't just do one discipline in order to know what to do.
• 27:42
  SPEAKER_00: So it might be that you do some engineering as a know-how, but you also do business because you have to know about operations.
• 27:48
  SPEAKER_00: Or it might be that you're interested in understanding the market, but you realize I need to understand people.
• 27:54
  SPEAKER_00: So you do something in the tech, but you do cognitive science because you need to know how people buy things or how customers will view your information.
• 28:03
  SPEAKER_00: And so I find our law.
• 28:05
  SPEAKER_00: I want to do law because I have to understand this IP stuff, but I want to do biology because I'm interested in pharma.
• 28:11
  SPEAKER_00: It's just so that they can create the knowledge base within themselves to ask the questions that they think they need to ask.
• 28:18
  SPEAKER_01: Do you find that a lot of students are coming in looking to, you know, as as you have across your career, affect positive change through technology?
• 28:30
  SPEAKER_00: Not through entrepreneurship.
• 28:32
  SPEAKER_01: No, no, I'm sorry, but through through through uh through higher education.
• 28:36
  SPEAKER_00: Yes.
• 28:37
  SPEAKER_00: Um I do find that the students of today, and again, this is not a full label across the board, but I find that the students that are coming to university today are much more aware of society and things that they can do to help within whatever it is that they are doing.
• 28:56
  SPEAKER_00: Um that is, I would say, a little bit different than say 20, 30, 40 years is I come into the university, um, I still want a job.
• 29:07
  SPEAKER_00: Now I come into the university, but I'm coming also with all this perception of what it means to be a citizen of whatever state I'm in, whatever country I live in, which is an interesting dynamic.
• 29:20
  SPEAKER_00: Um it means I may not work for certain companies versus others.
• 29:24
  SPEAKER_01: Yeah.
• 29:24
  SPEAKER_01: You and you and I actually now I I'm just remembering this, but I think you and I actually discussed this before that this was I mean, this is somewhat off topic, but that this was kind of the perceived, I guess, culture clash at the time was um, you know, younger people coming into the workforce and there being like friction between what like ethically what they wanted and what they thought the companies themselves represented.
• 29:49
  SPEAKER_00: Yes.
• 29:50
  SPEAKER_00: Um and I will say that we still have that dynamic, except that the one thing is that companies are not catering as much to that dynamic.
• 30:02
  SPEAKER_01: Um it is what it is.
• 30:05
  SPEAKER_01: Yeah, things have things have things have changed.
• 30:07
  SPEAKER_01: Uh yeah.
• 30:08
  SPEAKER_01: I we we won't we won't we won't go into the specifics of that, but certainly that is something that um has played a role in a lot of your work, and I'm thinking it's like specifically your new book, it's coming out in November, is coming out at an interesting time as we're speaking about this.
• 30:25
  SPEAKER_01: And par partially because you know several years ago we were having the conversation.
• 30:32
  SPEAKER_01: This this is like honestly, this is like the um melting ice caps conversation.
• 30:36
  SPEAKER_01: Several years ago we were having the conversation about biases and AI a lot, and it just seems like we're not for whatever reason having it as much as um we we were before.
• 30:46
  SPEAKER_01: What what just to sort of like start us off, what what is the current state of that conversation abstractly?
• 30:55
  SPEAKER_00: Um so I would say abstractly, this concept of bias in AI has been muted.
• 31:02
  SPEAKER_00: Um and in fact, uh, and and for various reasons, uh especially in the United States.
• 31:08
  SPEAKER_00: Um even the think, the thought around how do you regulate AI has been muted, uh especially in the United States.
• 31:16
  SPEAKER_00: Um but yet people still have fears.
• 31:19
  SPEAKER_00: And so there's now this thing around people have fears about AI because of the biases, but you can't necessarily voice what those biases are.
• 31:28
  SPEAKER_00: Um and I actually think that we are at this kind of crossroad where those biases, and we can just say the differences of how AI interacts with different people, is actually going to affect everyone and everything.
• 31:43
  SPEAKER_00: Um, in fact, in the book, uh it's called Rebooting the Machines, I talk about things like military AI and the fact that AI is in its at some point, maybe even by the time November comes, uh AI will be able to actually uh pull the trigger.
• 32:02
  SPEAKER_00: So what does that mean if you then still have this um inaccuracies in facial recognition or inaccuracies in identifying social economics?
• 32:12
  SPEAKER_00: Well, it means that when AI pulls a trigger, you might have a lot more impact depending on who you are.
• 32:19
  SPEAKER_00: That's real-world problems.
• 32:21
  SPEAKER_01: This is something I come back to a lot is that you know, there are so many with every advance, especially as we get into things like AI, there's so many philosophical questions, and and there have been, but you know, it seems like, well, technology just kind of pushes on because that's sort of the the state of the world.
• 32:40
  SPEAKER_01: And you um and I'm curious because most of the most interesting conversations that I've had with are are folks who when they went through school, they they obviously they did all the computer science and everything, but also they you know they studied the humanities or they studied philosophy.
• 32:59
  SPEAKER_01: And it sounds like you had that that side of things.
• 33:01
  SPEAKER_01: And how much did the humanities part play a role in your thinking around these issues?
• 33:07
  SPEAKER_00: I mean the the humanities, the liberal arts approach to engineering uh has been really central to everything that I do and how I think about technology.
• 33:18
  SPEAKER_00: You know, I consider myself uh I focus on the human-centered robotics, human-robot interaction.
• 33:24
  SPEAKER_00: Um, and I think it's because I I chose, I mentioned I chose Brown University.
• 33:29
  SPEAKER_00: It is a liberal arts institution.
• 33:31
  SPEAKER_00: As an engineer, we were taught to think about liberal arts as core.
• 33:35
  SPEAKER_00: It allows you to have critical thinking.
• 33:37
  SPEAKER_00: You read a book, I remember, I'm an engineer, I'm reading a book, and you're like, okay, analyze this passage.
• 33:43
  SPEAKER_00: I'm like, it's a passage, it's words.
• 33:45
  SPEAKER_00: It's like, no, no, no.
• 33:46
  SPEAKER_00: Like, what is your perception around it?
• 33:48
  SPEAKER_00: And the entire class, every single person, all eight had a different perception.
• 33:52
  SPEAKER_00: And then you had a conversation about that.
• 33:54
  SPEAKER_00: I mean, so when I think about technology, even my philosophy around every technology is dual use.
• 34:01
  SPEAKER_00: There's good and there's bad.
• 34:03
  SPEAKER_00: And it's up to us to ensure that the good is enhanced, but there will always still be bad.
• 34:08
  SPEAKER_00: Like that philosophy comes from that humanities perspective, looking through the liberal arts and viewing my technology in the same lens.
• 34:16
  SPEAKER_00: So very, very important.
• 34:17
  SPEAKER_01: Yeah, I mean, it it gets complicated because who defines what's good and what's bad, right?
• 34:23
  SPEAKER_00: And that transitions and that changes.
• 34:25
  SPEAKER_00: I mentioned having eight people in my class, like those are eight different viewpoints.
• 34:30
  SPEAKER_00: So which one is correct?
• 34:32
  SPEAKER_00: I would say all of them and none of them.
• 34:34
  SPEAKER_00: What is good?
• 34:36
  SPEAKER_00: It's going to be from your perspective, from your country, from your state, from your home.
• 34:42
  SPEAKER_00: You define what's good, and as long as your good does not impede someone else's good, I think we have a conversation.
• 34:49
  SPEAKER_00: Constructive dialogue, civil discourse, all of those words are about how do we have a conversation about what is good that we both can accept so that we both have benefit.
• 35:00
  SPEAKER_01: Yeah, I I I think it's I don't know, maybe I could be wrong, but it seems to be increasingly more difficult to find a good that doesn't impede on somebody else's good.
• 35:08
  SPEAKER_00: You know, this is where I sometimes I think I I differ is when we think about good and impeding on someone else's good, we all consider it as a relative.
• 35:18
  SPEAKER_01: Or a zero-sum game, maybe, yeah.
• 35:20
  SPEAKER_00: A zero-sum game, and I actually don't think of it as a zero-sum game.
• 35:24
  SPEAKER_00: Um, I think of if if I'm at a hundred percent and someone else is at 10%, okay, if would you accept 90% for that other person to get 20%?
• 35:36
  SPEAKER_00: That's a zero-sum game.
• 35:38
  SPEAKER_00: I actually think of like, okay, we're at 10% and 100%.
• 35:42
  SPEAKER_00: Let's figure out how do we get to 150%.
• 35:44
  SPEAKER_00: I I don't, I think the world is too big and there's too many opportunities not to open up the pie or bake another pie.
• 35:52
  SPEAKER_01: So so, like, certainly like this book and and your last book deal with a lot of these large philosophical questions.
• 35:59
  SPEAKER_01: Um what's your sense of what kind of some, I guess, pragmatic approaches to solving the questions might be?
• 36:08
  SPEAKER_00: Well, one of the nice things about this new book is that there are steps that every person can do.
• 36:13
  SPEAKER_00: Oh.
• 36:13
  SPEAKER_00: So much more practical.
• 36:14
  SPEAKER_00: So there's the philosophical, but there's the what can you actually do yourself?
• 36:18
  SPEAKER_00: Um, everything from, you know, there are bounty programs where you can identify biases in AI and you get paid for it?
• 36:25
  SPEAKER_00: Like, like, why don't you contribute to that?
• 36:27
  SPEAKER_00: Did you know that uh if you're really thinking about accessibility and disability, there's ways that you can report to Waze or to Google if you find something that's not accessible to a certain target demographic, you can actually report that.
• 36:41
  SPEAKER_00: Put it on the map.
• 36:42
  SPEAKER_00: Like, why shouldn't you do that, even if you have a if you don't have a disability?
• 36:46
  SPEAKER_00: So some very practical methods, um, even in terms of your chatbots and your use of chatbots.
• 36:53
  SPEAKER_00: I always say uh a Apple a day keeps the bugs away, while a uh basically a vacation day from chatbots can also keep the bugs away in the same form or fashion.
• 37:05
  SPEAKER_01: Yeah, I I just had this conversation with somebody and it was interesting, and and this goes to, you know, it it's often I mean, I'm sure that this is well, I know this is something that you're like acutely aware of that um we often don't consider other people's biases until they're like pointed out to us, right?
• 37:25
  SPEAKER_01: Because they're not part of our our lived experience.
• 37:28
  SPEAKER_01: And um somebody in a recent interview I did, somebody pointed out to me that okay, well, you know, we've talked about biases around AI traditionally, around you know, things like gender and race, and now that physical AI is becoming a thing, accessibility is even more of an issue as physical AI is entering the real world.
• 37:48
  SPEAKER_00: Yes, it is.
• 37:49
  SPEAKER_00: Um, and I think that um when I think about accessibility, and that's an area that I also focus on because I work with children with special needs, motor disabilities, most of the world does not think about it until they realize that they need some element.
• 38:07
  SPEAKER_00: And statistics-wise, if you're over the age of 60, you have a higher probability of needing services, needing something that we would consider accessibility just because our body ages.
• 38:17
  SPEAKER_00: Um, and so if you like to live long, you need to really be concerned about accessibility.
• 38:22
  SPEAKER_00: Um, but I think when we have physical AI, there's a couple of things.
• 38:25
  SPEAKER_00: One is robotics, these systems encounter the same hindrances, the same barriers as someone who has a disability.
• 38:35
  SPEAKER_00: And so things about, okay, how do I get into a building when the ramp is on the other side of the building?
• 38:44
  SPEAKER_00: Okay, now a robot has to figure out how to climb stairs.
• 38:47
  SPEAKER_00: If there was a ramp at every single front of the building, you know what?
• 38:51
  SPEAKER_00: We wouldn't have to think about how do you have robots climb up stairs.
• 38:54
  SPEAKER_01: I've been saying we wouldn't be having this legs versus wheels conversation if more places were ADA compliant.
• 39:00
  SPEAKER_00: Exactly, but it's not.
• 39:02
  SPEAKER_00: And so um these are the things.
• 39:05
  SPEAKER_00: I truly believe as the world becomes more accessible, more concerned about ensuring that we have universally designed structures and buildings and even computers, uh, robots will have an easier job of interacting and being embedded in our world.
• 39:21
  SPEAKER_01: I'm I'm curious to know a bit more about your work with um uh special people with special needs.
• 39:28
  SPEAKER_01: I guess I maybe specifically children with special needs, because they, you know, you obviously, when we first logged on, got excited about keep on, and keep on at least early on, was uh built for uh potential children, yeah, with autism.
• 39:41
  SPEAKER_01: Was that something were you also working in that field?
• 39:44
  SPEAKER_00: We yes, yes.
• 39:46
  SPEAKER_00: Um keep on, we were looking at because it's very emotive.
• 39:50
  SPEAKER_00: Uh, and so we're looking at it as a communication system uh with children with autism.
• 39:56
  SPEAKER_00: But now what we're looking at is, um, and this is a continuing project for a number of years, is how do we create a low-cost robot playmate, movement coach, uh that can go into the home and interact with children with cerebral palsy to do their movement exercises.
• 40:14
  SPEAKER_00: Um we have now a number of eight-week studies that have shown improvement of this robot interacting with children through a game.
• 40:23
  SPEAKER_00: So they they're playing games together, movement games, upper arm movement games.
• 40:27
  SPEAKER_00: Um, and it's low cost, i.e., the platform we're able to produce for under 300, and that's in part, uh, but under$300.
• 40:39
  SPEAKER_00: I'm actually very supportive of it, uh, very excited about it.
• 40:44
  SPEAKER_00: Because then it could also address the needs of older adults.
• 40:47
  SPEAKER_00: It could address even athletes that have get injured.
• 40:51
  SPEAKER_00: It could address their their needs, uh, but we're we're validating it with children.
• 40:57
  SPEAKER_01: Uh, we as in uh as in the school, as in a company?
• 41:01
  SPEAKER_00: Oh, so the we is I work with my clinical collaborators who's the PI.
• 41:05
  SPEAKER_00: Um, so this is Yupin Ching, Chen, and she's at Georgia State University.
• 41:11
  SPEAKER_01: Okay.
• 41:11
  SPEAKER_00: Uh, and then we also have a company that's creating um the the robot.
• 41:16
  SPEAKER_00: Hello Robot, I think is the name of the company.
• 41:18
  SPEAKER_00: They make the um the low-cost platform for us.
• 41:21
  SPEAKER_00: And it's gone through a couple of revisions, but the one we have now is perfect.
• 41:27
  SPEAKER_00: We can even connect to a large language model, so we've been experimenting with that.
• 41:32
  SPEAKER_01: Yeah, it it's it's wild just how many things.
• 41:35
  SPEAKER_01: I mean, you know, we you were speaking earlier about how like all you know the foundations for all these technologies were here for a really long time, but even sort of the most like technical, like non-emotive people that I speak to in the field still seem like they were just completely blown away by what LLMs have brought to the field.
• 41:56
  SPEAKER_00: And but you know what I will say, uh they should be.
• 42:00
  SPEAKER_00: And and I don't know if you recall when the ChatGPT came out on the world, we've been using the platforms as in terms of APIs.
• 42:11
  SPEAKER_00: I mean, I had a couple of papers like years before, at least two years before, that talked about um, and we were looking at, of course, biases and in some of the um kind of the the hate assessment online algorithms.
• 42:26
  SPEAKER_00: But it was very much, you had to be a researcher, you had to be a computer scientist to use it.
• 42:33
  SPEAKER_00: And you pair up the methods that were already existed with a conversational aspect, which is the human-centered part.
• 42:40
  SPEAKER_00: It's like, oh wow, look at that.
• 42:42
  SPEAKER_00: And now it can learn.
• 42:44
  SPEAKER_00: Um, and is learning in the wild.
• 42:46
  SPEAKER_00: And it's learning pretty darn fast.
• 42:48
  SPEAKER_00: That's kind of the scary part.
• 42:50
  SPEAKER_00: It's learning really, really fast.
• 42:52
  SPEAKER_00: And I I anyone who says that they were not surprised, I would say is maybe fibbing a little bit.
• 43:00
  SPEAKER_01: How colorful are you being when you say scary?
• 43:04
  SPEAKER_00: I my fear is actually around these LLMs and agentic AI.
• 43:11
  SPEAKER_00: That is my super, super, super fearful scariness.
• 43:16
  SPEAKER_00: Um, because we are not putting in the guardrails necessary.
• 43:21
  SPEAKER_00: Um, and so it could be something simple as it wipes out all of the financial backing services.
• 43:28
  SPEAKER_00: So I don't know how much money I have anymore.
• 43:31
  SPEAKER_01: Something simple like that.
• 43:34
  SPEAKER_00: Right?
• 43:34
  SPEAKER_00: Because if you don't put in the guardrails, it it could.
• 43:37
  SPEAKER_00: I mean, we've seen a couple of stories about you know hard drives being, you know, wiped clean, um, all the way to what happens when we have AI that can pull the trigger.
• 43:49
  SPEAKER_00: And now we add in agentic AI where we don't have guardrails.
• 43:52
  SPEAKER_00: Um, and so I fear that because I don't see the guardrails being put in.
• 43:58
  SPEAKER_01: All right.
• 43:58
  SPEAKER_01: Well, like I said, we started on a downer, so we'll end not on a downer.
• 44:04
  SPEAKER_01: Um Thank you.
• 44:05
  SPEAKER_01: We'll try, we'll attempt.
• 44:07
  SPEAKER_01: As we're talking about all of these things that you've been working on, just technology in general, physical AI, uh, AI, what are you seeing right now that's on the horizon that's making you hopeful?
• 44:19
  SPEAKER_00: Honestly, the push around, and I won't say humanoid, but uh the humanoid robots.
• 44:26
  SPEAKER_00: And the reason is is if they can get it right, like humanoid and logistics centers, it's an overkill.
• 44:32
  SPEAKER_00: But with all the investment, if they can actually figure out the manipulation, the grasping problem coupled with the mobile, basically mobile manipulation, if that can be figured out, or even figure out the hardware and how do you learn new applications, and here's the here's the game book, it allows us to put these devices in the hospitals and in the clinic and in the nursing homes and in different environments where I think that they are needed.
• 45:00
  SPEAKER_00: So that's that's actually hopeful.
• 45:01
  SPEAKER_00: It's school systems where we don't necessarily have enough teachers in the K through 12 that can interact with all the kids on a personalized level.
• 45:10
  SPEAKER_00: I mean, there's so many great opportunities if this can be resolved and fixed, and we can lower the hardware costs and the software costs, and we have a game book of how to do this.
• 45:21
  SPEAKER_00: That actually makes me really optimistic.
• 45:24
  SPEAKER_01: So so that idea that you had, you know, in the late 80s about separating you know HIV needles, it only took 40 years and like$10 trillion.
• 45:38
  SPEAKER_00: That's all.
• 45:40
  SPEAKER_00: That's it.
• 45:41
  SPEAKER_00: Just something small.
• 45:43
  SPEAKER_00: Well, Dr.
• 45:43
  SPEAKER_00: Howard, thank you so much for joining us.
• 45:46
  SPEAKER_00: Thank you.
• 45:47
  SPEAKER_00: Thank you for having me.
• 45:48
  SPEAKER_01: Thank you so much to Dr.
• 45:50
  SPEAKER_01: Howard and Matt at OSU for setting that up.
• 45:53
  SPEAKER_01: Thanks to you as always for sticking around and listening.
• 45:56
  SPEAKER_01: If you are a fan of the show, you can support us by liking and subscribing on the platform of your choice and subscribing to our newsletter.
• 46:04
  SPEAKER_01: That's over at automated.fm.
• 46:07
  SPEAKER_01: And with all of that, we will see you next week for another episode of Automated.